Director, Pharmacometrics

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Job Description

Takeda is a global, values-based, R&D-driven, top 10 biopharmaceutical leader committed to discover and deliver life-transforming treatments, guided by our commitment to patients, our people and the planet.

Our Data and Quantitative Sciences group (DQS) is made up of more than 500 quantitative scientists who harness the insight of data and digital to speed the development of highly innovative treatments to patients. These scientists (from quantitative clinical pharmacology, statistics, programming, outcomes research and epidemiology, patient safety & pharmacovigilance, digital strategy, library sciences, and data architecture/governance) bring their expertise to our global program teams and reimagine our disciplines. They work with novel data streams, including real-world data and digital tools, and apply advanced analytics including artificial intelligence and automation.

As part of DQS, the Pharmacometrics team at Takeda comprises scientists who work across multiple therapeutic areas, partnering closely with QCP leads to develop and execute Model-Informed Drug Development (MIDD) strategies for development programs across the portfolio.

Position Objectives:

• Leads and accountable for the delivery of asset level pharmacometrics strategies,
• Provides additional portfolio support through program reviews, collaborative decision-making, infrastructure and best practice initiatives.
• Recognized expert in a specific pharmacometrics methodology/field.
• Explores and excels in synergistic relationships with experts in digital health, global outcomes/epidemiology, biostatistics, and other key data science disciplines.
• Serves as an ambassador of Pharmacometrics, Quantitative Clinical Pharmacology (QCP) and DQS to the R&D organization and the external scientific community through high-value participation at scientific meetings and impactful publications.

Accountabilities:

• Defines, leads and drives an asset level pharmacometrics strategy to support advancing the asset and decision making milestones; influencing the project and clinical teams.
• Leads and drives in-house modelling capabilities such c-QTc analysis automation, MBMA, Clinical Trial Simulation, Mechanistic Modelling.
• Key driver of pharmacometrics strategies, including Model-Informed Drug Development (MIDD) as part of the overall QDM framework integrating pharmacometrics plan in collaboration with QCP and SQS.
• Represents Pharmacometrics, where required in global or regional regulatory agency meetings and be responsible for the technical component of the clinical pharmacology summary documents for regulatory submissions.
• Oversees and/or independently performs PK, PD, and pharmacometric analyses including the interpretation of PK/PD data in close partnership with DSI and external partners
• Maintains a high standard for good clinical practice, compliance, and ethics.
• Provides mentorship to junior staff, acts as a cross functional leader and may manage a sub-team within a TA.
• Participates as a member of Business Development due diligence, when requested.
• Leads infrastructure initiatives and/ or cross-functional best practice initiatives.
• Demonstrates leadership in scientific societies and cross-industry consortiums related to the pharmacometrics work streams within ACCP, ASCPT, ACOP, PAGE, ISQP, IQ, and DIA.

Education & Competencies

• Pharm D. or PhD with 8+ years of working experience in a quantitative field with working experience in pharmacometrics, mechanistic modelling, statistics or chemical bio-engineering
• MS with 12+ years' working experience in a quantitative field with working experience in pharmacometrics, mechanistic modelling, statistics or chemical bio-engineering.
• Established pharmacometrician, recognized domain expert in a particular methodology or area e.g. safety and its application to drug development.
• Expert in use of some of the relevant softwares e.g. R, NonMem, Stan, SAS, Pheonix.
• Experience in leading and driving the scoping and implementation of MIDD within projects and at an asset/platform level.
• Hands-on expertise/knowledge in application of advanced knowledge in integration of multiple data sources (PK/PD/Efficacy/Safety/biomarkers/RWE/RWD) across the asset to drive dose selection, optimized trial design, and quantitative decision making.
• Solid mathematical and/or statistics background.
• Strong knowledge of allied fields critical for drug development, such as statistics or clinical pharmacology
• Ability to evaluate Benefit-Risk associated with project decisions, effectively manage GPT expectations, and influence TA strategies.

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